The　concept　of　＇brain　age＇,　derived　from　neuroimaging　data,　serves　as　a　crucial　biomarker　reflecting　cognitive　vitality　and　neurodegenerative　trajectories.　In　the　past　decade,　machine　learning　(ML)　and　deep　learning　(DL)　integration　has　transformed　the　field,　providing　advanced　models　for　brain　age　estimation.　However,　achieving　precise　brain　age　prediction　across　all　ages　remains　a　significant　analytical　challenge.　This　comprehensive　review　scrutinizes　advancements　in　ML-　and　DL-based　brain　age　prediction,　analyzing　52　peer-reviewed　studies　from　2020　to　2024.　It　assesses　various　model　architectures,　highlighting　their　effectiveness　and　nuances　in　lifespan　brain　age　studies.　By　comparing　ML　and　DL,　strengths　in　forecasting　and　methodological　limitations　are　revealed.　Finally,　key　findings　from　the　reviewed　articles　are　summarized　and　a　number　of　major　issues　related　to　ML/DL-based　lifespan　brain　age　prediction　are　discussed.　Through　this　study,　we　aim　at　the　synthesis　of　the　current　state　of　brain　age　prediction,　emphasizing　both　advancements　and　persistent　challenges,　guiding　future　research,　technological　advancements,　and　improving　early　intervention　strategies　for　neurodegenerative　diseases.